Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data

arXiv:2405.05424v1 Announce Type: new
Abstract: Non-parametric models, such as Gaussian Processes (GP), show promising results in the analysis of complex data. Their applications in neuroscience data have recently gained traction. In this research, we introduce a novel neural decoder model built upon GP models. The core idea is that two GPs generate neural data and their associated labels using a set of low- dimensional latent variables. Under this modeling assumption, the latent variables represent the underlying manifold or essential features …

abstract analysis applications arxiv core cs.lg data decoder decoding gaussian processes neuroscience non-parametric novel parametric process processes research results show type